1. Field of the Invention
The present invention relates to the field of obstacle detection, and, more particularly, to real-time obstacle detection with a calibrated camera and known ego-motion.
2. Description of the Related Art
Real-time obstacle detection in a moving vehicle is a key component for autonomous driving and computer vision-based driving assistance. Obstacle detection in autonomous driving and driving assistance is often used to avoid collision. An obstacle is generally context dependent. That is, although an obstacle on the road is usually a vehicle, it can be something else, such as a large rock.
In Serge Beucher et al., Road Monitoring and Obstacle Detection System by Image Analysis and Mathematical Morphology, Proceedings 5th EAEC (European Automobile Engineers Cooperation) International Congress, “The European Automotive Industry Meets the Challenges of the Year 2000”, Strasbourg, 21-23 Jun. 1995, March 1995, obstacle detection is performed with morphological operations using the road/lane detection results. The obstacle is assumed to be a vehicle with two distinct features: (a) it has lower dark parts such as a bumper or shadows, and (b) it has a regular rectangular shape. Problems with this method include its strong assumption that the obstacle must be a vehicle and its dependency on correct road and/or lane detection.
In Massimo Bertozzi et al., Sensing of Automotive Environments Using Stereo Vision, 30th International Symposium on Automotive Technology and Automation (ISATA), Special Session on Machine Vision and Intelligent Vehicles and Autonomous Robots, pages 187-193, Florence, Italy, Jun. 16-19, 1997, an inverse perspective mapping (IPM) is used to obtain a top view of the road plane for each of two stereo cameras. The difference of the two remapped images shows the high rising obstacles off the road plane. This method requires more than one camera, and it cannot detect obstacles with small heights moving on the road.
In Heinrich Niemann et al., Integrated motion and geometry based obstacle detection in image sequences of traffic scenes, 10th Annual Symposium AeroSense '96, Orlando, Fla., 1996, a current image is back projected to a road plane in a three-dimensional world system using a current transformation between a camera and the real world. Vehicle movement information is applied to obtain the location of the road in the previous frame time. Then the road is mapped back to the image using the transformation in the previous frame. The registered image with ego-motion cancelled out is compared with the previous image to find obstacles. This method requires warping of the current frame image to a previous frame image with ego-motion correcting. This method also requires increased computation power. It also does not use information from other sensors in the vehicle, such as the front wheel angle to get more accurate vehicle movement status.